CN116543838A - Data analysis method for biological gene selection expression probability - Google Patents
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Abstract
The invention relates to the technical field of biological gene detection, in particular to a data analysis method of biological gene selection expression probability. Combining the difference of each expression target object, and determining the influence of each influencing substance on the expression probability of each expression target object yield; and establishing a convolutional neural network training set, and determining the optimal quantity of each influencing substance influencing the expression target object. The invention determines the difference of the expressed targets, establishes a convolutional neural network training set, determines the optimal quantity of each influencing substance for influencing the expressed targets, determines the influence degree of the expressed probability of the expressed targets through the change of the concentration of each influencing substance in the verification process, provides a reference basis for the culture of the original matters in the later period, determines the influence interval of the concentration of each influencing substance on the yield of the expressed targets according to the peak value of the yield of the expressed targets and the corresponding current influencing substance concentration, compares the data of each comparison group, and determines the corresponding concentration of each influencing substance when the yield of the expressed targets is optimal.
Description
Technical Field
The invention relates to the technical field of biological gene detection, in particular to a data analysis method of biological gene selection expression probability.
Background
The selective expression of biological genes is a phenomenon in which organisms express different parts of their genome at different stages and different parts of their development, through regulation of gene levels, transcription levels, etc., and as a result, cell differentiation and development of individuals are completed, and genes are selectively expressed under specific temporal and spatial conditions during cell differentiation, with the result that cells having different morphological structures and physiological functions are formed.
The cell culture dish is a special solution for supporting cell growth and maintaining cell stability, is an important system in chemical, biological and pharmacological science research, and is also an important foundation for developing cell activity and new drugs, and the main components of the cell culture dish comprise water, nutrient components and existence factors.
In the data prediction process of the biological gene selection expression probability, most of the existing verification modes are manually performed to perform experiment operation, influence substances for maintaining cell survival are added into a cell culture dish, the influence of different influence substances on the biological gene selection expression probability is determined by changing the concentration of the influence substances, the types of the influence substances added into the cell culture dish are more, the influence substances required by different cells are different, and the task amount is too heavy and the efficiency is lower through manual stepwise analysis experiment.
In order to cope with the above problems, a data analysis method of the selective expression probability of biological genes is demanded.
Disclosure of Invention
The invention aims to provide a data analysis method of biological gene selection expression probability so as to solve the problems in the background technology.
In order to achieve the above object, there is provided a data analysis method of a selective expression probability of a biological gene, comprising the steps of:
s1, planning a visual monitoring area of a culture dish, configuring a microscopic visual monitoring module, and performing visual monitoring on the expression process of the culture dish;
s2, collecting characteristic information of each expression target object, combining the characteristic information of the expression target object by the microscopic vision monitoring module, identifying the expression target object in the culture dish, and recording the output of the expression target object;
s3, determining various influencing substances in the culture process of the original substance;
s4, planning the initial quantity of each influencing substance, meeting the living requirement of the culture original object, and generating blank group data;
s5, defining the unit adjustment quantity of each influencing material, and adopting a single variable principle to perform single variable adjustment according to the unit adjustment quantity of the influencing material;
s6, recording a single variable adjustment result by the microscopic vision monitoring module, determining the output of the expressed target object, generating control group data, comparing the control group data with blank group data, and determining the difference of the expressed target object;
s7, combining the differences of each expression target object, and determining the influence of each influencing substance on the expression probability of each expression target object yield;
s8, building a convolutional neural network training set, and determining the optimal quantity of each influence substance to influence the expression target object.
As a further improvement of the present technical solution, the method for planning a visual monitoring area of a culture dish in S1 includes the following steps:
s1.1, determining a concentrated position of an expression target object, and marking the concentrated position as a concentrated monitoring area;
s1.2, adjusting the height of the monitoring point according to the definition of the feedback picture of the microscopic vision monitoring module.
As a further improvement of the technical scheme, the method for collecting the characteristic information of each expression target object in the step S2 comprises the following steps:
s2.1, establishing characteristic information base of each expression product;
s2.2, determining corresponding characteristics of each expression product according to the expression product characteristic information base, and distinguishing the expression products through the characteristics.
As a further improvement of the present technical solution, the influencing substances in S3 include a temperature factor, a PH value factor, a presence factor, a nutrient solution concentration, an aqueous solution concentration, and a reference solution concentration.
As a further improvement of the present technical solution, the method for planning the initial amounts of the influencing substances in S4 includes the following steps:
s4.1, determining initial culture quantity of an original substance;
s4.2, collecting all the content of the influence substances necessary for the original substance existing in the body, and generating an original substance influence substance content database;
s4.3, planning the initial quantity of each influencing substance in the culture dish by combining the initial culture quantity of the original substance.
As a further improvement of the present technical solution, the single variable principle in S5 includes the following steps:
s5.1, determining an influence substance to be verified, and marking the influence substance as a variable influence substance;
s5.2, determining the quantity of the rest influencing substances before the adjustment of the variable influencing substances, and marking the quantity as a constant influencing substance;
s5.3, defining a normal range of the variation quantity of the expression target object, and planning the variable to influence the unit adjustment quantity of the substance according to the normal range of the variation quantity of the expression target object.
As a further improvement of the technical scheme, the method for planning the variable influencing the unit adjustment quantity of the substance in S5.3 adopts an average score algorithm, and the algorithm formula comprises the following steps:
step one, determining the normal range of the variation of the expression target object, and marking as；
Step two, determining the initial expression target object quantity under the state of variable influencing substance initial quantityAnd determining the superposition expression target object quantity in the superposition variable influence mass quantity state formed after the superposition unit adjustment quantity M>Calculating the amount of the target substance of the superposition expression +.>And the amount of the initially expressed target substance->Is marked as +.>；
Step three, whenThe output unit adjustment amount is M;
when (when)The output unit adjustment amount is->Determining the initial amount of the variable-affecting substance plus the unit adjustment amount +.>The resulting median expressed target amount +.>；
Step four, repeating the step three until the median value expresses the target object quantityAnd outputting the corresponding superposition unit adjustment.
As a further improvement of the present technical solution, the method for determining the influence of each influencing material on the expression probability of each expression target product yield in S7 includes the following steps:
a first step of determining initial amounts of various influencing substances through the second stepRatio of unit adjustment M corresponding thereto +.>；
Step two, determining the superposition expression target object quantity generated after each influencing substance superposition position adjustment quantity MCalculating influence value of each influence substance>；
Thirdly, comparing the influence values of the influence substances, and sequencing the influence of the influence substances on the expression target.
As a further improvement of the present technical solution, the convolutional neural network training set in S8 includes an input layer, a convolutional layer, and an output layer.
Compared with the prior art, the invention has the beneficial effects that:
in the data analysis method of the biological gene selection expression probability, the difference of the expression target objects is determined, a convolutional neural network training set is established, the optimal quantity of each influencing substance influencing the expression target objects is determined, the influence degree of the expression target object expression probability is determined through the change of each influencing substance concentration in the verification process, a reference basis is provided for the subsequent culture of the original substances, the influence interval of each influencing substance concentration on the expression target object yield is determined according to the peak value of the expression target object yield and the corresponding current influencing substance concentration, and the corresponding influencing substance concentrations when the expression target object yield is optimal are determined by comparing the data of each comparison group.
Drawings
FIG. 1 is a flowchart illustrating the overall process steps of the present invention;
FIG. 2 is a schematic diagram of the overall operation of the present invention;
FIG. 3 is a diagram of the steps of a method of planning a visual monitoring area of a culture dish according to the present invention;
FIG. 4 is a step diagram of a method for collecting characteristic information of various expression targets according to the invention;
FIG. 5 is a diagram of steps in a method of planning initial amounts of impact substances according to the present invention;
FIG. 6 is a single variable principle step diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, a method for analyzing data of probability of selective expression of biological genes is provided, which comprises the following steps:
s1, planning a visual monitoring area of a culture dish, configuring a microscopic visual monitoring module, and performing visual monitoring on the expression process of the culture dish;
s2, collecting characteristic information of each expression target object, combining the characteristic information of the expression target object by the microscopic vision monitoring module, identifying the expression target object in the culture dish, and recording the output of the expression target object;
s3, determining various influencing substances in the culture process of the original substance;
s4, planning the initial quantity of each influencing substance, meeting the living requirement of the culture original object, and generating blank group data;
s5, defining the unit adjustment quantity of each influencing material, and adopting a single variable principle to perform single variable adjustment according to the unit adjustment quantity of the influencing material;
s6, recording a single variable adjustment result by the microscopic vision monitoring module, determining the output of the expressed target object, generating control group data, comparing the control group data with blank group data, and determining the difference of the expressed target object;
s7, combining the differences of each expression target object, and determining the influence of each influencing substance on the expression probability of each expression target object yield;
s8, building a convolutional neural network training set, and determining the optimal quantity of each influence substance to influence the expression target object.
When the method is specifically used, in the biological gene selection expression probability verification process, firstly, a culture dish visual monitoring area is planned, a microscopic visual monitoring module is configured to perform visual monitoring on the culture dish expression process, each item of expression target object characteristic information is collected, the microscopic visual monitoring module is combined with the expression target object characteristic information to identify the expression target object in the culture dish, the expression target object output is recorded, each item of the expression target object is positioned through each item of characteristic information, each item of influence substances in the original object culture process is determined, each item of influence substance initial quantity is planned, the survival requirement of the culture original object is met, blank group data is generated, each item of influence substance unit adjustment quantity is specified, single variable adjustment is performed according to the influence substance unit adjustment quantity by adopting a single variable principle, namely, each time one influencing substance is regulated, the concentration of the rest influencing substances is unchanged, each time the influencing substance of the unit regulating quantity concentration is regulated, a microscopic vision monitoring module records each time a single variable regulating result, the output of an expressed target is determined, control group data is generated, the control group data and blank group data are compared, the difference of the expressed target is determined, then the difference of each expressing target is combined, the influence of each influencing substance on the expression probability of each expressing target output is determined, a comparison library of the increasing and decreasing interval of the influencing substance and the corresponding output of the expressed target is established, the change point of the output of the expressed target (for example, the change from an increasing interval to a decreasing interval) is determined, the unit regulating quantity is re-planned, the peak point of the output of the expressed target and the corresponding concentration of the current influencing substance are determined, thereby a convolutional neural network training set is established, the optimal amount of each influencing substance influencing the expression target is determined.
In addition, the method for planning the visual monitoring area of the culture dish in the step S1 comprises the following steps:
s1.1, determining a concentrated position of an expression target object, and marking the concentrated position as a concentrated monitoring area;
s1.2, adjusting the height of the monitoring point according to the definition of the feedback picture of the microscopic vision monitoring module.
In the process of planning and planning a visual monitoring area, as different expression targets are different in expression rules and expression quantity, the centralized positions of the different expression targets are changed along with the different expression rules and the different expression quantity, at the moment, the centralized positions of the expression targets are determined by determining the expression rules and the expression quantity of each expression target, the centralized positions of the expression targets are marked as a centralized monitoring area, a microscopic visual monitoring module is configured in the centralized monitoring area, and meanwhile, the heights of monitoring points are adjusted according to the definition of a feedback picture of the microscopic visual monitoring module, so that each expression target can be clearly observed by the fed-back monitoring picture.
Further, the method for collecting the characteristic information of each expression target object in the S2 comprises the following steps:
s2.1, establishing characteristic information base of each expression product;
s2.2, determining corresponding characteristics of each expression product according to the expression product characteristic information base, and distinguishing the expression products through the characteristics.
In the process of identifying the expression target object, firstly, various expression product characteristic information bases, such as the state, color, shape and the like of the expression target object in a microscopic state, are established, then corresponding characteristics of each expression product are determined according to the expression product characteristic information bases, and expression product distinction is carried out through the characteristics for carrying out statistics on expression product amounts in later stages.
Still further, the influencing substances in S3 include a temperature factor, a PH value factor, a presence factor, a nutrient solution concentration, an aqueous solution concentration, and a reference solution concentration, wherein the temperature factor is a temperature of the culture original of the culture dish;
the PH value factor is the PH value of the culture original substance of the culture dish;
presence factor: for maintaining the health of the original substance and regulating the permeability of the original substance cell membrane;
concentration of nutrient solution: nutrients including amino acids and carbohydrates for imparting energy to the original for survival;
concentration of aqueous solution: the concentration of the added aqueous solution in the culture dish is used for maintaining the activity of the original matters;
reference solution concentration: special substances, such as antibiotics, required for the different originals.
Specifically, the method for planning the initial amounts of each influencing material in S4 includes the following steps:
s4.1, determining initial culture quantity of an original substance;
s4.2, collecting all the content of the influence substances necessary for the original substance existing in the body, and generating an original substance influence substance content database;
s4.3, planning the initial quantity of each influencing substance in the culture dish by combining the initial culture quantity of the original substance.
In the process of planning the initial quantity of each influencing substance, as the original substance to be verified is separated from the body of the original substance, each influencing substance which is necessary when the original substance exists in the body is required to be added into the culture dish, the initial culture quantity of the original substance is firstly determined, then the content of each influencing substance which is necessary for the original substance to exist in the body is collected, an original substance influencing substance content database is generated, the initial quantity of each influencing substance in the culture dish is planned by combining the initial culture quantity of the original substance, and is used as each influencing substance quantity for maintaining the survival of the original substance, blank group data is formed for later control experiments, and the influence of each influencing substance quantity on the expression target of the original substance is singly analyzed.
In addition, the single variable principle in S5 includes the following steps:
s5.1, determining an influence substance to be verified, and marking the influence substance as a variable influence substance;
s5.2, determining the quantity of the rest influencing substances before the adjustment of the variable influencing substances, and marking the quantity as a constant influencing substance;
s5.3, defining a normal range of the variation quantity of the expression target object, and planning the variable to influence the unit adjustment quantity of the substance according to the normal range of the variation quantity of the expression target object.
In the process of variable adjustment, in order to ensure single variability, firstly, determining an influencing substance to be verified, marking the influencing substance as a variable, then determining the quantity of the rest influencing substances before the variable influencing substance is adjusted, marking the influencing substance as a constant influencing substance, wherein the quantity of the constant influencing substance is always kept unchanged as an independent variable, prescribing a normal range of the variable quantity of an expressed target object, planning the variable influencing substance unit adjustment quantity according to the normal range of the variable quantity of the expressed target object, for example, when the variable influencing substance unit adjustment quantity is overlarge, the variable quantity of the expressed target object sharply increases or the original object is caused to stop being expressed, and the method for planning the variable influencing substance unit adjustment quantity at the moment is invalid, so that the verification effect cannot be achieved, and only when the expressed target object is in a normal change state, the influence on the original object in the expression process can be determined.
Further, the method for planning the variable to influence the unit adjustment quantity of the substance in S5.3 adopts an average score algorithm, and the algorithm formula comprises the following steps:
step one, determining the normal range of the variation of the expression target object, and marking as；
Step two, determining the initial expression target object quantity under the state of variable influencing substance initial quantityAnd determining the superposition expression target object quantity in the superposition variable influence mass quantity state formed after the superposition unit adjustment quantity M>Calculating the amount of the target substance of the superposition expression +.>And the amount of the initially expressed target substance->Is marked as +.>；
Step three, whenThe output unit adjustment amount is M;
when (when)The output unit adjustment amount is->Determining the initial amount of the variable-affecting substance plus the unit adjustment amount +.>The resulting median expressed target amount +.>；
Step four, repeating the step three until the median value tableTo the target amountAnd outputting the corresponding superposition unit adjustment.
Still further, the method for determining the influence of each influencing substance on the expression probability of each expression target object yield in S7 comprises the following steps:
step one, determining the initial quantity of each influencing material through step twoThe ratio of the unit adjustment quantity M corresponding to the ratio；
Step two, determining the superposition expression target object quantity generated after each influencing substance superposition position adjustment quantity MCalculating influence value of each influence substance>；
Thirdly, comparing the influence values of the influence substances, and sequencing the influence of the influence substances on the expression target.
In the process of determining the influence of each influencing substance on the expression probability of each expression target object yield, the initial quantity of each influencing substance is determinedRatio of unit adjustment M corresponding thereto +.>Subsequently determining the amount of the target substance of the superposition expression which is produced after the adjustment of the superposition position of the influencing substances M>Calculating the influence value of each influence substanceAnd finally, comparing the influence values of the influence substances, sequencing the influence of the influence substances on the expression target object, and sequentially determining the influence degree of the influence substances.
In addition, the convolutional neural network training set in S8 includes an input layer, a convolutional layer, and an output layer, where:
input layer: for inputting initial culture amount of original substance, blank data, constant influencing substance, variable influencing substance, initial expression target amountUnit adjustment M, influence substance initial quantity +.>Normal range of expression target variable；
Convolution layer: planning and superposing the expression target object quantityA calculation step of determining the planning superposition expression target quantity +.>Whether or not it belongs to the normal range of the variation of the expression target substance->Planning ratio calculation algorithm ∈>Generating an influence substance influence value calculation algorithm +.>；
Output layer: and outputting corresponding numerical values according to each calculation result, and binding each variable data.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for analyzing data of a biological gene selection expression probability, comprising the steps of:
s1, planning a visual monitoring area of a culture dish, configuring a microscopic visual monitoring module, and performing visual monitoring on the expression process of the culture dish;
s2, collecting characteristic information of each expression target object, combining the characteristic information of the expression target object by the microscopic vision monitoring module, identifying the expression target object in the culture dish, and recording the output of the expression target object;
s3, determining various influencing substances in the culture process of the original substance;
s4, planning the initial quantity of each influencing substance, meeting the living requirement of the culture original object, and generating blank group data;
s5, defining the unit adjustment quantity of each influencing material, and adopting a single variable principle to perform single variable adjustment according to the unit adjustment quantity of the influencing material;
s6, recording a single variable adjustment result by the microscopic vision monitoring module, determining the output of the expressed target object, generating control group data, comparing the control group data with blank group data, and determining the difference of the expressed target object;
s7, combining the differences of each expression target object, and determining the influence of each influencing substance on the expression probability of each expression target object yield;
s8, building a convolutional neural network training set, and determining the optimal quantity of each influence substance to influence the expression target object.
2. The method for analyzing data of selective expression probability of biological genes according to claim 1, wherein: the method for planning the visual monitoring area of the culture dish in the step S1 comprises the following steps:
s1.1, determining a concentrated position of an expression target object, and marking the concentrated position as a concentrated monitoring area;
s1.2, adjusting the height of the monitoring point according to the definition of the feedback picture of the microscopic vision monitoring module.
3. The method for analyzing data of selective expression probability of biological genes according to claim 1, wherein: the method for collecting the characteristic information of each expression target object in the S2 comprises the following steps:
s2.1, establishing characteristic information base of each expression product;
s2.2, determining corresponding characteristics of each expression product according to the expression product characteristic information base, and distinguishing the expression products through the characteristics.
4. The method for analyzing data of selective expression probability of biological genes according to claim 1, wherein: the influencing substances in the S3 comprise temperature factors, PH value factors, existence factors, nutrient solution concentration, aqueous solution concentration and reference solution concentration.
5. The method for analyzing data of selective expression probability of biological genes according to claim 1, wherein: the method for planning the initial quantity of each influencing material in the S4 comprises the following steps:
s4.1, determining initial culture quantity of an original substance;
s4.2, collecting all the content of the influence substances necessary for the original substance existing in the body, and generating an original substance influence substance content database;
s4.3, planning the initial quantity of each influencing substance in the culture dish by combining the initial culture quantity of the original substance.
6. The method for analyzing data of selective expression probability of biological genes according to claim 1, wherein: the single variable principle in S5 comprises the following steps:
s5.1, determining an influence substance to be verified, and marking the influence substance as a variable influence substance;
s5.2, determining the quantity of the rest influencing substances before the adjustment of the variable influencing substances, and marking the quantity as a constant influencing substance;
s5.3, defining a normal range of the variation quantity of the expression target object, and planning the variable to influence the unit adjustment quantity of the substance according to the normal range of the variation quantity of the expression target object.
7. The method for analyzing data of probability of selective expression of biological genes according to claim 6, wherein: the method for planning the variable to influence the unit adjustment quantity of the substance in S5.3 adopts an average score algorithm, and an algorithm formula of the method comprises the following steps:
step one, determining the normal range of the variation of the expression target object, and marking as；
Step two, determining the initial expression target object quantity under the state of variable influencing substance initial quantityAnd determining the superposition expression target object quantity in the superposition variable influence mass quantity state formed after the superposition unit adjustment quantity M>Calculating the amount of the target substance of the superposition expression +.>And the amount of the initially expressed target substance->Is marked as +.>；
Step three, whenOutput unitThe adjustment amount is M;
when (when)The output unit adjustment amount is->Determining the initial quantity of the variable influencing material and the unit adjustment quantityThe resulting median expressed target amount +.>；
Step four, repeating the step three until the median value expresses the target object quantityAnd outputting the corresponding superposition unit adjustment.
8. The method for analyzing data of probability of selective expression of biological genes according to claim 7, wherein: the method for determining the influence of each influencing substance on the expression probability of each expression target object yield in the S7 comprises the following steps:
a first step of determining initial amounts of various influencing substances through the second stepThe ratio of the unit adjustment quantity M corresponding to the ratio；
Step two, determining the superposition expression target object quantity generated after each influencing substance superposition position adjustment quantity MCalculating influence value of each influence substance>；
Thirdly, comparing the influence values of the influence substances, and sequencing the influence of the influence substances on the expression target.
9. The method for analyzing data of probability of selective expression of biological genes according to claim 8, wherein: the convolutional neural network training set in the S8 comprises an input layer, a convolutional layer and an output layer.
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